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All Journal Semantik Seminar Nasional Informatika (SEMNASIF) Journal of Information System JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURIKOM (Jurnal Riset Komputer) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF)
Fikri Budiman
Departemen Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Implementation of Discrete Wavelet Transform and Directed Acyclic Graph SVM for Batik Pattern Recognition Edi Sugiarto; Fikri Budiman; Amiq Fahmi; MY Teguh Sulistyono; Asih Rohmani
JOINS (Journal of Information System) Vol 10 No 1 (2025): Edisi Mei 2025
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v10i1.12576

Abstract

Batik as a heritage of the ancestors of the Indonesian nation certainly needs to be preserved so that it continues to be recognized from generation to generation, one of which is by introducing the diversity of its patterns. Efforts to introduce batik patterns can be made, one of which is by implementing technology that can recognize batik patterns automatically based on batik patterns, namely pattern recognition technology. This study aims to optimize batik pattern recognition using the discrete wavelet transform (DWT) and directed acyclic graph SVM (DAGSVM) methods. The stages start from preprocessing, feature extraction, and classification. The study used 310 batik images of 7 different patterns and divided into 240 images for training data and 70 for testing data. DWT method is used in the feature extraction stage while DAG SVM is used in the classification stage. The study was conducted by comparing the accuracy between standard DAG SVM and DAG SVM that has been optimized with DWT and the results of the accuracy test can be proven that adding the DWT method with DAG SVM can increase accuracy by 3%.
Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,Random Forest, Dan Gradient Boosting Akhmadi Akhmadi; Fikri Budiman
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9369

Abstract

House price prediction is a crucial issue in the property sector because it is influenced by various interrelated factors, such as building characteristics and environmental conditions. Accurate prediction using conventional approaches is often difficult and can lead to errors in decision-making. Therefore, this study aims to develop and compare the performance of house price prediction models using three machine learning algorithms: Linear Regression, Random Forest, and Gradient Boosting. The dataset used is the Home Value Insights Dataset on Kaggle, which consists of 1,000 houses with eight main attributes. The research stages include data pre-processing, dividing training and test data, model training, parameter optimization using GridSearchCV, and performance evaluation based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics using the 10-Fold Cross Validation method. The test results show that Linear Regression provides the best performance with an R² value of 0.8539 and a lower prediction error rate than Random Forest and Gradient Boosting. Although the ensemble model shows competitive results, increasing model complexity does not result in a significant increase in accuracy, so Linear Regression is considered the simplest, most efficient, and most easily interpreted approach for house price prediction systems on datasets with characteristics that tend to be linear.
Analisis Perbandingan Metode Random Forest, XGBoost, dan Logistic Regression Untuk Klasifikasi Deteksi Dini Penyakit Diabetes Novriansyah Afqi Nur Akmal Fauzi; Fikri Budiman
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9392

Abstract

Diabetes Mellitus is a chronic disease with a continuously increasing prevalence, posing serious challenges to public health and contributing significantly to the global economic burden. The often non-specific nature of early symptoms increases the risk of delayed diagnosis, highlighting the need for accurate early detection approaches to support clinical decision-making. This study aims to analyze and compare the performance of three machine learning algorithms Logistic Regression, Random Forest, and XGBoost in classifying diabetes risk based on several clinical parameters, including age, body mass index (BMI), blood pressure, glucose level, and HbA1c. The dataset used in this research was obtained from the Diabetes Prediction Dataset, consisting of 100,000 records. The research process involved handling missing data, applying One-Hot Encoding to categorical variables, normalizing numerical features, and addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics to provide a comprehensive assessment. The experimental results indicate that XGBoost achieved the best performance, with an accuracy of 96.88% and a ROC-AUC value of 98.00%. Meanwhile, Random Forest attained an accuracy of 95.68% with an F1-Score of 74.76%, while Logistic Regression recorded an accuracy of 88.96% and the highest recall value of 89.12%. These findings suggest that ensemble learning methods, particularly boosting approaches, are more effective in improving the accuracy of diabetes and non-diabetes classification. The primary contribution of this study lies in providing a multi-metric comparative analysis that can serve as a reference for selecting the most effective machine learning model in the development of medical decision support systems for early diabetes detection.
Analisis Komparatif Model Random Forest dan XGBoost untuk Klasifikasi Penyakit Jantung Berbasis Data Klinis Angga Guardi Zunus Saputra; Fikri Budiman
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9427

Abstract

Heart disease remains a major global health challenge due to its increasing prevalence, highlighting the need for accurate and reliable early diagnostic systems. This study aims to analyze and compare the performance of Random Forest (RF) and XGBoost algorithms for heart disease classification, and to identify the most suitable model for data-driven clinical decision support. Addressing a research gap in ensemble learning studies, this research conducts a comprehensive comparative evaluation using the UCI Heart Disease Dataset. The proposed methodology includes data preprocessing, feature encoding, normalization, class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter optimization based on RandomizedSearchCV. Model performance is evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and Matthews Correlation Coefficient (MCC), supported by Feature Importance analysis. The results demonstrate that both ensemble models achieve strong predictive performance, with consistently high F1-scores above 0.88. XGBoost exhibits superior overall performance, achieving the highest F1-score of 0.8995 and Precision of 0.8785, making it more effective in minimizing False Positive predictions. In contrast, Random Forest shows superior sensitivity, with the highest Recall of 0.9510 and ROC-AUC of 0.9582, along with better cross-validation stability. These findings indicate that the selection of heart disease classification algorithms should be aligned with specific clinical objectives, and the results of this study are expected to contribute to the development of effective machine learning–based clinical decision support systems.
Co-Authors Ahmad Zainul Fanani Ajib Susanto Akhmadi Akhmadi Angga Guardi Zunus Saputra Ari Yahya A Arry Maulana Syarif Asih Rohmani Asih Rohmani Asih Rohmani Asih Rohmani Defri Kurniawan Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Elkaf Rahmawan Pramudya Esmi Nur Fitri Esmi Nur Fitri Fahmi Amiq Junta Zeniarja Khafiidz Hastuti Linda Marlinda M Hafidz Ariansyah Moch Arief Soeleman Muhammad Rafi Haqqi Ramadhan Muslih Muslih MY Teguh Sulistyono Novriansyah Afqi Nur Akmal Fauzi Ruri Suko Basuki S Nurhendratno Slamet Sudaryanto N Sri Winarno Widyatmoko Karis Yosiko Aditya Pratama